Chapter 8 Introduction to Pattern
Discovery
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Chapter 8 Introduction to Pattern Discovery
8.1 Introduction
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8.1 Introduction
Pattern Discovery
The Essence of Data Mining?
“…the discovery of interesting,
unexpected, or valuable
structures in large data sets.”
– David Hand
3
...
There are a multitude of definitions for the field of data mining and knowledge discovery. Most center
on the concept of pattern discovery. For example, David Hand, Professor of Statistics at Imperial College,
London and a noted data mining authority, defines the field as “…the discovery of interesting, unexpected,
or valuable structures in large data sets.” (Hand 2005) This is made possible by the ever-increasing data
stores brought about by the era’s information technology.
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Chapter 8 Introduction to Pattern Discovery
Pattern Discovery
The Essence of Data Mining?
“…the discovery of interesting,
unexpected, or valuable
structures in large data sets.”
– David Hand
“If you’ve got terabytes of data, and you’re
relying on data mining to find interesting
things in there for you, you’ve lost before
you’ve even begun.”
– Herb Edelstein
4
While Hand’s pronouncement is grandly promising, experience has shown it to be overly optimistic.
Herb Edelstein, President of Two Crows Corporation and an internationally recognized expert in data
mining, data warehousing, and CRM, counters with the following (Beck (Editor) 1997):
“If you’ve got terabytes of data, and you’re relying on data mining to find interesting things in there
for you, you’ve lost before you’ve even begun. You really need people who understand what it is they
are looking for – and what they can do with it once they find it.”
Many people think data mining (in particular, pattern discovery) means magically finding hidden nuggets
of information without having to formulate the problem and without regard to the structure or content of
the data. This is an unfortunate misconception.
8.1 Introduction
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Pattern Discovery Caution






Poor data quality
Opportunity
Interventions
Separability
Obviousness
Non-stationarity
5
In his defense, David Hand is well aware of the limitations of pattern discovery and provides guidance
on how these analyses can fail (Hand 2005). These failings often fall into one of six categories:
 Poor data quality assumes many guises: inaccuracies (measurement or recording errors), missing,
incomplete or outdated values, and inconsistencies (changes of definition). Patterns found in false data
are fantasies.
 Opportunity transforms the possible to the perceived. Hand refers to this as the problem of
multiplicity, or the law of truly large numbers. Examples of this abound. Hand notes the odds of a
person winning the lottery in the United States are extremely small and the odds of that person winning
it twice are fantastically so. However, the odds of someone in the United States winning it twice (in a
given year) are actually better than even. As another example, you can search the digits of pi for
“prophetic” strings such as your birthday or significant dates in history and usually find them, given
enough digits (www.angio.net/pi/piquery).
 Intervention, that is, taking action on the process that generates a set of data, can destroy or distort
detected patterns. For example, fraud detection techniques lead to preventative measures, but the
fraudulent behavior often evolves in response to this intervention.
 Separability of the interesting from the mundane is not always possible, given the information found
in a data set. Despite the many safeguards in place, it is estimated that credit card companies lose $0.18
to $0.24 per $100 in online transactions (Rubinkam 2006).
 Obviousness in discovered patterns reduces the perceived utility of an analysis. Among the patterns
discovered through automatic detection algorithms, you find that there is an almost equal number of
married men as married women, and you learn that ovarian cancer occurs primarily in women and that
check fraud occurs most often for customers with checking accounts.
 Non-stationarity occurs when the process that generates a data set changes of its own accord. In such
circumstances, patterns detected from historic data can simply cease. As Eric Hoffer states, “In times
of change, learners inherit the Earth, while the learned find themselves beautifully equipped to deal
with a world that no longer exists.”
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Chapter 8 Introduction to Pattern Discovery
Pattern Discovery Applications
Data reduction
Novelty detection
Profiling
Market basket analysis
A C
B
6
Sequence analysis
...
Despite the potential pitfalls, there are many successful applications of pattern discovery:
 Data reduction is the most ubiquitous application, that is, exploiting patterns in data to create a more
compact representation of the original. Though vastly broader in scope, data reduction includes analytic
methods such as cluster analysis.
 Novelty detection methods seek unique or previously unobserved data patterns. The methods find
application in business, science, and engineering. Business applications include fraud detection,
warranty claims analysis, and general business process monitoring.
 Profiling is a by-product of reduction methods such as cluster analysis. The idea is to create rules that
isolate clusters or segments, often based on demographic or behavioral measurements. A marketing
analyst might develop profiles of a customer database to describe the consumers of a company’s
products.
 Market basket analysis, or association rule discovery, is used to analyze streams of transaction data
(for example, market baskets) for combinations of items that occur (or do not occur) more (or less)
commonly than expected. Retailers can use this as a way to identify interesting combinations of
purchases or as predictors of customer segments.
 Sequence analysis is an extension of market basket analysis to include a time dimension to the
analysis. In this way, transactions data is examined for sequences of items that occur (or do not occur)
more (or less) commonly than expected. A Webmaster might use sequence analysis to identify patterns
or problems of navigation through a Web site.
8.1 Introduction
Pattern Discovery Tools
Data reduction
Novelty detection
Profiling
Market basket analysis
A C
B
Sequence analysis
...
7
The first three pattern discovery applications are primarily served (in no particular order) by three tools
in SAS Enterprise Miner: Cluster, SOM/Kohonen, and Segment Profile. The next section features a
demonstration of the Cluster and Segment Profile tools.
Pattern Discovery Tools
Data reduction
Novelty detection
Profiling
Market basket analysis
A C
B
Sequence analysis
8
Market basket analysis and sequence analysis are performed by the Association tool. The Path Analysis
tool can also be used to analyze sequence data. (An optional demonstration of the Association tool is
presented at the end of this chapter.)
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Chapter 8 Introduction to Pattern Discovery
8.2 Cluster Analysis
Unsupervised Classification
inputs
grouping
cluster 1
cluster 2
cluster 3
cluster 1
Unsupervised classification:
grouping of cases based on
similarities in input values.
cluster 2
10
...
Unsupervised classification (also known as clustering and segmenting) attempts to group training data set
cases based on similarities in input variables. It is a data reduction method because an entire training data
set can be represented by a small number of clusters. The groupings are known as clusters or segments,
and they can be applied to other data sets to classify new cases. It is distinguished from supervised
classification (also known as predictive modeling), which is discussed in previous chapters.
The purpose of clustering is often description. For example, segmenting existing customers into groups
and associating a distinct profile with each group might help future marketing strategies. However, there
is no guarantee that the resulting clusters will be meaningful or useful.
Unsupervised classification is also useful as a step in predictive modeling. For example, customers can be
clustered into homogenous groups based on sales of different items. Then a model can be built to predict
the cluster membership based on more easily obtained input variables.
8.2 Cluster Analysis
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k-means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Re-assign cases.
6. Repeat steps 4 and 5
until convergence.
12
One of the most commonly used methods for clustering is the k-means algorithm. It is a straightforward
algorithm that scales well to large data sets and is, therefore, the primary tool for clustering in SAS
Enterprise Miner.
While often overlooked as an important part of a clustering process, the first step in using the k-means
algorithm is to choose a set of inputs. In general, you should seek inputs that have the following
attributes:
 are meaningful to the analysis objective
 are relatively independent
 are limited in number
 have a measurement level of Interval
 have low kurtosis and skewness statistics (at least in the training data)
Choosing meaningful inputs is clearly important for interpretation and explanation of the generated
clusters. Independence and limited input count make the resulting clusters more stable. (Small
perturbations of training data usually do not result in large changes to the generated clusters.) An interval
measurement level is recommended for k-means to produce non-trivial clusters. Low kurtosis and
skewness statistics on the inputs avoid creating single-case outlier clusters.
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k-means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Re-assign cases.
6. Repeat steps 4 and 5
until convergence.
13
The next step in the k-means algorithm is to choose a value for k, the number of cluster centers.
SAS Enterprise Miner features an automatic way to do this, assuming that the data has k distinct
concentrations of cases. If this is not the case, you should choose k to be consistent with your
analytic objectives.
With k selected, the k-means algorithm chooses cases to represent the initial cluster centers (also named
seeds).
8.2 Cluster Analysis
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k-means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
...
14
The Euclidean distance from each case in the training data to each cluster center is calculated. Cases are
assigned to the closest cluster center.

Because the distance metric is Euclidean, it is important for the inputs to have compatible
measurement scales. Unexpected results can occur if one input’s measurement scale differs
greatly from the others.
k-means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
15
...
The cluster centers are updated to equal the average of the cases assigned to the cluster
in the previous step.
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Chapter 8 Introduction to Pattern Discovery
k-means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
...
16
Cases are reassigned to the closest cluster center.
k-means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
17
...
8.2 Cluster Analysis
k-means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
...
18
The update and reassign steps are repeated until the process converges.
k-means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
25
...
On convergence, final cluster assignments are made. Each case is assigned to a unique segment.
The segment definitions can be stored and applied to new cases outside of the training data.
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Chapter 8 Introduction to Pattern Discovery
Segmentation Analysis
Training Data
When no clusters exist,
use the k-means
algorithm to partition
cases into contiguous
groups.
27
While they are often used synonymously, a segmentation analysis is distinct from a traditional cluster
analysis. A cluster analysis is geared toward identifying distinct concentrations of cases in a data set.
When no distinct concentrations exist, the best you can do is a segmentation analysis, that is,
algorithmically partitioning the input space into contiguous groups of cases.
Demographic Segmentation Demonstration
Analysis goal:
Group geographic regions into segments based on
income, household size, and population density.
Analysis plan:




Select and transform segmentation inputs.
Select the number of segments to create.
Create segments with the Cluster tool.
Interpret the segments.
28
The following demonstration illustrates the use of clustering tools. The goal of the analysis is to group
people in the United States into distinct subsets based on urbanization, household size, and income
factors. These factors are common to commercial lifestyle and life-stage segmentation products.
(For examples, see www.claritas.com or www.spectramarketing.com.)
8.2 Cluster Analysis
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Segmenting Census Data
This demonstration introduces SAS Enterprise Miner tools and techniques for cluster and segmentation
analysis. There are five parts:
 define the diagram and data source
 explore and filter the training data
 integrate the Cluster tool into the process flow and select the number of segments to create
 run a segmentation analysis
 use the Segment Profile tool to interpret the analysis results
Diagram Definition
Use the following steps to define the diagram for the segmentation analysis.
1. Right-click Diagrams in the Project panel and select Create Diagram. The Create New Diagram
window opens and requests a diagram name.
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2. Type Segmentation Analysis in the Diagram Name field and select OK. SAS Enterprise
Miner creates an analysis workspace window named Segmentation Analysis.
You use the Segmentation Analysis window to create process flow diagrams.
8.2 Cluster Analysis
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Data Source Definition
Follow these steps to create the segmentation analysis data source.
1. Right-click Data Sources in the Project panel and select Create Data Source. The Data Source
Wizard opens.
The Data Source Wizard guides you through a seven-step process to create a SAS Enterprise Miner
data source.
2. Select Next > to use a SAS table as the source for the metadata. (This is the usual choice.)
The Data Source Wizard proceeds to Step 2. In this step, select the SAS table that you want to make
available to SAS Enterprise Miner. You can either type the library name and SAS table name as
libname.tablename or select the SAS table from a list.
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3. Select Browse… to choose a SAS table from the libraries visible to the SAS Foundation server.
The Select a SAS Table dialog box opens.
One of the libraries listed is named AAEM61, which is the library name defined in the project start-up
code.
8.2 Cluster Analysis
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4. Select the Aaem61 library and the Census2000 SAS table.
The Census2000 data is a postal code-level summary of the entire 2000 United States Census.
It features seven variables:
ID
postal code of the region
LOCX
region longitude
LOCY
region latitude
MEANHHSZ
average household size in the region
MEDHHINC
median household income in the region
REGDENS
region population density percentile (1=lowest density, 100=highest density)
REGPOP
number of people in the region
The data is suited for creation of life-stage, lifestyle segments using SAS Enterprise Miner’s pattern
discovery tools.
5. Select OK.
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The Select a SAS Table dialog box closes and the selected table is entered in the Table field.
6. Select Next >. The Data Source Wizard proceeds to Step 3.
This step of the Data Source Wizard provides basic information about the selected table.
8.2 Cluster Analysis
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7. Select Next >. The Data Source Wizard proceeds to Step 4.
Step 4 of the Data Source Wizard starts the metadata definition process. SAS Enterprise Miner
assigns initial values to the metadata based on characteristics of the selected SAS table. The Basic
setting assigns initial values to the metadata based on variable attributes such as the variable name,
data type, and assigned SAS format. The Advanced setting also includes information about the
distribution of the variable to assign the initial metadata values.
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8. Select Next > to use the Basic setting. The Data Source Wizard proceeds to Step 5.
Step 5 of the Data Source Wizard enables you to specify the role and level for each variable in the
selected SAS table. A default role is assigned based on the name of a variable. For example, the
variable ID was given the role ID based on its name. When a variable does not have a name
corresponding to one of the possible variable roles, it will, using the Basic setting, be given the
default role of input. An input variable is used for various types of analysis to describe a
characteristic, measurement, or attribute of a record, or case, in a SAS table.
The metadata settings are correct for the upcoming analysis.
8.2 Cluster Analysis
9. Select Next >. The Data Source Wizard proceeds to Step 6.
Step 6 in the Data Source Wizard enables you to set a role for the selected SAS table and provide
descriptive comments about the data source definition.
For the impending analysis, a table role of Raw is acceptable.
Step 7 provides summary information about the created data set.
10. Select Finish to complete the data source definition. The CENSUS2000 table is added to the
Data Sources entry in the Project panel.
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Chapter 8 Introduction to Pattern Discovery
Exploring and Filtering Analysis Data
A worthwhile next step in the process of defining a data source is to explore and validate its contents.
By assaying the prepared data, you substantially reduce the chances of erroneous results in your analysis,
and you can gain insights graphically into associations between variables.
Data Source Exploration
1. Right-click the CENSUS2000 data source and select Edit Variables… from the shortcut menu.
The Variables - CENSUS2000 dialog box opens.
2. Examine histograms for the available variables.
3. Select all listed inputs by dragging the cursor across all of the input names or by holding down the
CTRL key and typing A.
8.2 Cluster Analysis
4. Select Explore…. The Explore window opens, and this time displays histograms for all of the
variables in the CENSUS2000 data source.
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Chapter 8 Introduction to Pattern Discovery
5. Maximize the MeanHHSize histogram by double-clicking its title bar. The histogram now fills the
Explore window.
As before, increasing the number of histogram bins from the default of 10 increases your
understanding of the data.
8.2 Cluster Analysis
6. Right-click in the histogram window and select Graph Properties… from the shortcut menu.
The Properties - Histogram dialog box opens.
You can use the Properties - Histogram dialog box to change the appearance of the corresponding
histogram.
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7. Type 100 in the Number of X Bins field and select OK. The histogram is updated to have
100 bins.
There is a curious spike in the histogram at (or near) zero. A zero household size does not make sense
in the context of census data.
8. Select the bar near zero in the histogram.
8.2 Cluster Analysis
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9. Restore the size of the window by double-clicking the title bar of the MeanHHSize window.
The window returns to its original size.
The zero average household size seems to be evenly distributed across the longitude, latitude, and
density percentile variables. It seems concentrated on low incomes and populations, and also makes
up the majority of the missing observations in the distribution of Region Density. It is worthwhile
to look at the individual records of the explore sample.
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10. Maximize the CENSUS2000 data table.
11. Scroll in the data table until you see the first selected row.
Records 45 and 46 (among others) have the zero Average Household Size characteristic. Other fields
in these records also have unusual values.
8.2 Cluster Analysis
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12. Select the Average Household Size column heading twice to sort the table by descending values
in this field. Cases of interest are collected at the top of the data table.
Most of the cases with zero Average Household Size have zero or missing on the remaining nongeographic attributes. There are some exceptions, but it could be argued that cases such as this are not
of interest for analyzing household demographics. The next part of this demonstration shows how to
remove cases such as this from the subsequent analyses.
13. Close the Explore and Variables windows.
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Case Filtering
The SAS Enterprise Miner Filter tool enables you to remove unwanted records from an analysis.
Use these steps to build a diagram to read a data source and to filter records.
1. Drag the CENSUS2000 data source to the Segmentation Analysis workspace window.
2. Select the Sample tab to access the Sample tool group.
3. Drag the Filter tool (fourth from the left) from the tools pallet into the Segmentation Analysis
workspace window and connect it to the CENSUS2000 data source.
8.2 Cluster Analysis
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4. Select the Filter node and examine the Properties panel.
Based on the values of the properties panel, the node will, by default, filter cases in rare levels in any
class input variable and cases exceeding three standard deviations from the mean on any interval
input variable.
Because the CENSUS2000 data source only contains interval inputs, only the Interval Variables
criterion is considered.
5. Change the Default Filtering Method property to User-Specified Limits.
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6. Select the Interval Variables ellipsis (…). The Interactive Interval Filter window opens.
You are warned at the top of the dialog box that the Train or raw data set does not exist.
This indicates that you are restricted from the interactive filtering elements of the node, which
are available after a node is run. You can, nevertheless, enter filtering information.
8.2 Cluster Analysis
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7. Type 0.1 as the Filter Lower Limit value for the input variable MeanHHSz.
8. Select OK to close the Interactive Interval Filter dialog box. You are returned to the SAS Enterprise
Miner interface window.
All cases with an average household size less than 0.1 will be filtered from subsequent analysis steps.
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9. Run the Filter node and view the results. The Results window opens.
10. Go to line 38 in the Output window.
Number Of Observations
Data
Role
Filtered
Excluded
DATA
TRAIN
32097
1081
33178
The Filter node removed 1081 cases with zero household size.
11. Close the Results window. The CENSUS2000 data is ready for segmentation.
8.2 Cluster Analysis
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Setting Cluster Tool Options
The Cluster tool performs k-means cluster analyses, a widely used method for cluster and segmentation
analysis. This demonstration shows you how to use the tool to segment the cases in the CENSUS2000
data set.
1. Select the Explore tab.
2. Locate and drag a Cluster tool into the diagram workspace.
3. Connect the Filter node to the Cluster node.
To create meaningful segments, you need to set the Cluster node to do the following:
 ignore irrelevant inputs
 standardize the inputs to have a similar range
4. Select the Variables… property for the Cluster node. The Variables window opens.
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5. Select Use  No for LocX, LocY, and RegPop.
The Cluster node creates segments using the inputs MedHHInc, MeanHHSz, and RegDens.
Segments are created based on the (Euclidean) distance between each case in the space of selected
inputs. If you want to use all the inputs to create clusters, these inputs should have similar
measurement scales. Calculating distances using standardized distance measurements (subtracting the
mean and dividing by the standard deviation of the input values) is one way to ensure this. You can
standardize the input measurements using the Transform Variables node. However, it is easier to use
the built-in property in the Cluster node.
8.2 Cluster Analysis
6. Select the inputs MedHHInc, MeanHHSz, and RegDens and select Explore…. The Explore
window opens.
The inputs selected for use in the cluster are on three entirely different measurement scales.
They need to be standardized if you want a meaningful clustering.
7. Close the Explore window.
8. Select OK to close the Variables window.
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Chapter 8 Introduction to Pattern Discovery
9. Select Internal Standardization  Standardization. Distances between points are calculated based
on standardized measurements.

Another way to standardize an input is by subtracting the input’s minimum value and
dividing by the input’s range. This is called range standardization. Range standardization
rescales the distribution of each input to the unit interval, [0,1].
The Cluster node is ready to run.
8.2 Cluster Analysis
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Creating Clusters with the Cluster Tool
By default, the Cluster tool attempts to automatically determine the number of clusters in the data.
A three-step process is used.
Step 1 A large number of cluster seeds are chosen (50 by default) and placed in the input space. Cases
in the training data are assigned to the closest seed, and an initial clustering of the data is
completed. The means of the input variables in each of these preliminary clusters are substituted
for the original training data cases in the second step of the process.
Step 2 A hierarchical clustering algorithm (Ward’s method) is used to sequentially consolidate the
clusters that were formed in the first step. At each step of the consolidation, a statistic named the
cubic clustering criterion (CCC) (Sarle 1983) is calculated. Then, the smallest number of clusters
that meets both of the following criteria is selected:
 The number of clusters must be greater than or equal to the number that is specified as the
Minimum value in the Selection Criterion properties.
 The number of clusters must have cubic clustering criterion statistic values that are greater than
the CCC threshold that is specified in the Selection Criterion properties.
Step 3 The number of clusters determined by the second step provides the value for k in a k-means
clustering of the original training data cases.
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1. Run the Cluster node and select Results…. The Results - Cluster window opens.
The Results - Cluster window contains four embedded windows.
 The Segment Plot window attempts to show the distribution of each input variable by cluster.
 The Mean Statistics window lists various descriptive statistics by cluster.
 The Segment Size window shows a pie chart describing the size of each cluster formed.
 The Output window shows the output of various SAS procedures run by the Cluster node.
Apparently, the Cluster node found four clusters in CENSUS2000 data. Because the number of
clusters is based on the cubic clustering criterion, it might be interesting to examine the values of this
statistic for various cluster counts.
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2. Select View  Summary Statistics  CCC Plot. The CCC Plot window opens.
In theory, the number of clusters in a data set is revealed by the peak of the CCC versus Number of
Clusters plot. However, when no distinct concentrations of data exist, the utility of the CCC statistic
is somewhat suspect. SAS Enterprise Miner attempts to establish reasonable defaults for its analysis
tools. The appropriateness of these defaults, however, strongly depends on the analysis objective and
the nature of the data.
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Specifying the Segment Count
You might want to increase the number of clusters created by the Cluster node. You can do this
by changing the CCC cutoff property or by specifying the desired number of clusters.
1. In the Properties panel for the Cluster node, select Specification Method  User Specify.
The User Specify setting creates a number of segments indicated by the Maximum Number
of Clusters property listed above it (in this case, 10).
8.2 Cluster Analysis
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2. Run the Cluster node and select Results…. The Results - Node: Cluster Diagram window opens, and
shows a total of 10 generated segments.
As seen in the Mean Statistics window, segment frequency counts vary from 10 cases to more than
9,000 cases.
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Exploring Segments
While the Results window shows a variety of data summarizing the analysis, it is difficult to understand
the composition of the generated clusters. If the number of cluster inputs is small, the Graph wizard can
aid in interpreting the cluster analysis.
1. Close the Results - Cluster window.
2. Select Exported Data from the Properties panel for the Cluster node. The Exported Data - Cluster
window opens.
This window shows the data sets that are generated and exported by the Cluster node.
8.2 Cluster Analysis
3. Select the Train data set and select Explore…. The Explore window opens.
You can use the Graph Wizard to generate a three-dimensional plot of the CENSUS2000 data.
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4. Select Actions  Plot. The Select a Chart Type window opens.
5. Select the icon for a three-dimensional scatter plot.
6. Select Next >. The Graph Wizard proceeds to the next step. Select Chart Roles.
8.2 Cluster Analysis
7. Select roles of X, Y, and Z for MeanHHSz, MedHHInc, and RegDens, respectively.
8. Select Role  Color for _SEGMENT_.
9. Select Finish.
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The Explore window opens with a three-dimensional plot of the CENSUS2000 data.
10. Rotate the plot by holding down the CTRL key and dragging the mouse.
Each square in the plot represents a unique postal code. The squares are color-coded by cluster
segment.
8.2 Cluster Analysis
To further aid interpretability, add a distribution plot of the segment number.
1. Select Action  Plot…. The Select a Chart Type window opens.
2. Select a Bar chart.
3. Select Next >.
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4. Select Role  Category for the variable _SEGMENT_.
5. Select Finish.
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A histogram of _SEGMENT_ opens.
By itself, this plot is of limited use. However, when the plot is combined with the three-dimensional
plot, you can easily interpret the generated segments.
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6. Select the tallest segment bar in the histogram, segment 4.
8.2 Cluster Analysis
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7. Select the three-dimensional plot. Cases corresponding to segment 4 are highlighted.
8. Rotate the three-dimensional plot to get a better look at the highlighted cases.
Cases in this largest segment correspond to households averaging between two and three members,
lower population density, and median household incomes between $20,000 and $50,000.
9. Close the Explore, Exported Data, and Results windows.
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Profiling Segments
You can gain a great deal of insight by creating plots as in the previous demonstration. Unfortunately,
if more than three variables are used to generate the segments, the interpretation of such plots becomes
difficult.
Fortunately, there is another useful tool in SAS Enterprise Miner for interpreting the composition of
clusters: the Segment Profile tool. This tool enables you to compare the distribution of a variable in an
individual segment to the distribution of the variable overall. As a bonus, the variables are sorted by how
well they characterize the segment.
1. Drag a Segment Profile tool from the Assess tool palette into the diagram workspace.
2. Connect the Cluster node to the Segment Profile node.
To best describe the segments, you should pick a reasonable subset of the available input variables.
8.2 Cluster Analysis
3. Select the Variables property for the Segment Profile node.
4. Select Use  No for ID, LocX, LocY, and RegPop.
5. Select OK to close the Variables dialog box.
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6. Run the Segment Profile node and select Results…. The Results - Node: Segment Profile Diagram
window opens.
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7. Maximize the Profile window.
Features of each segment become apparent. For example, segment 4, when compared to the overall
distributions, has a lower Region Density Percentile, more central Median Household Income, and
slightly higher Average Household Size.
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11. Maximize the Variable Worth: _SEGMENT_ window.
The window shows the relative worth of each variable in characterizing each segment. For example,
segment 4 is largely characterized by the RegDens input, but the other two inputs also play a role.
Again, similar analyses can be used to describe the other segments. The advantage of the Segment
Profile window (compared to direct viewing of the segmentation) is that the descriptions can be more
than three-dimensional.
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Exercises
1. Conducting Cluster Analysis
The DUNGAREE data set gives the number of pairs of four different types of dungarees sold at stores
over a specific time period. Each row represents an individual store. There are six columns in the data
set. One column is the store identification number, and the remaining columns contain the number of
pairs of each type of jeans sold.
Name
Model
Role
Measurement
Level
Description
STOREID
ID
Nominal
Identification number of the store
FASHION
Input
Interval
Number of pairs of fashion jeans sold at the store
LEISURE
Input
Interval
Number of pairs of leisure jeans sold at the store
STRETCH
Input
Interval
Number of pairs of stretch jeans sold at the store
ORIGINAL
Input
Interval
Number of pairs of original jeans sold at the store
SALESTOT
Rejected
Interval
Total number of pairs of jeans sold (the sum of
FASHION, LEISURE, STRETCH, and
ORIGINAL)
a. Create a new diagram in your project. Name the diagram Jeans.
b. Define the data set DUNGAREE as a data source.
c. Determine whether the model roles and measurement levels assigned to the variables are
appropriate.
Examine the distribution of the variables.
 Are there any unusual data values?
 Are there missing values that should be replaced?
d. Assign the variable STOREID the model role ID and the variable SALESTOT the model role
Rejected. Make sure that the remaining variables have the Input model role and the Interval
measurement level. Why should the variable SALESTOT be rejected?
e. Add an Input Data Source node to the diagram workspace and select the DUNGAREE data table
as the data source.
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f. Add a Cluster node to the diagram workspace and connect it to the Input Data node.
g. Select the Cluster node and select Internal Standardization  Standardization. What would
happen if you did not standardize your inputs?
h. Run the diagram from the Cluster node and examine the results.
Does the number of clusters created seem reasonable?
i. Specify a maximum of six clusters and rerun the Cluster node.
How does the number and quality of clusters compare to that obtained in part h?
j. Use the Segment Profile node to summarize the nature of the clusters.
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8.3 Market Basket Analysis (Self-Study)
Market Basket Analysis
A B C
A C D
Rule
AD
CA
AC
B&CD
38
B C D
Support
2/5
2/5
2/5
1/5
A D E
B C E
Confidence
2/3
2/4
2/3
1/3
...
Market basket analysis (also known as association rule discovery or affinity analysis) is a popular data
mining method. In the simplest situation, the data consists of two variables: a transaction and an item.
For each transaction, there is a list of items. Typically, a transaction is a single customer purchase, and the
items are the things that were bought. An association rule is a statement of the form (item set A)  (item
set B).
The aim of the analysis is to determine the strength of all the association rules among a set of items.
The strength of the association is measured by the support and confidence of the rule. The support for
the rule A  B is the probability that the two item sets occur together. The support of the rule A  B is
estimated by the following:
transactions that contain every item in A and B
all transactions
Notice that support is symmetric. That is, the support of the rule A  B is the same as the support of the
rule B  A.
The confidence of an association rule A  B is the conditional probability of a transaction containing
item set B given that it contains item set A. The confidence is estimated by the following:
transactions that contain every item in A and B
transactions that contain the items in A
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Implication?
Checking Account
No
Yes
No
500
3500
4,000
Yes
1000
5000
6,000
Savings
Account
Support(SVG  CK) = 50%
Confidence(SVG  CK) = 83%
Expected Confidence(SVG  CK) = 85%
Lift(SVG  CK) = 0.83/0.85 < 1
10,000
40
The interpretation of the implication () in association rules is precarious. High confidence and support
does not imply cause and effect. The rule is not necessarily interesting. The two items might not even be
correlated. The term confidence is not related to the statistical usage; therefore, there is no repeated
sampling interpretation.
Consider the association rule (saving account)  (checking account). This rule has 50% support
(5,000/10,000) and 83% confidence (5,000/6,000). Based on these two measures, this might be
considered a strong rule. On the contrary, those without a savings account are even more likely to have
a checking account (87.5%). Saving and checking are, in fact, negatively correlated.
If the two accounts were independent, then knowing that a person has a saving account does not help in
knowing whether that person has a checking account. The expected confidence if the two accounts were
independent is 85% (8,500/10,000). This is higher than the confidence of SVG  CK.
The lift of the rule A  B is the confidence of the rule divided by the expected confidence, assuming that
the item sets are independent. The lift can be interpreted as a general measure of association between the
two item sets. Values greater than 1 indicate positive correlation, values equal to 1 indicate zero
correlation, and values less than 1 indicate negative correlation. Notice that lift is symmetric. That is,
the lift of the rule A  B is the same as the lift of the rule B  A.
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Barbie Doll  Candy
1.
2.
3.
4.
5.
6.
7.
8.
Put them closer together in the store.
Put them far apart in the store.
Package candy bars with the dolls.
Package Barbie + candy + poorly selling item.
Raise the price on one, and lower it on the other.
Offer Barbie accessories for proofs of purchase.
Do not advertise candy and Barbie together.
Offer candies in the shape of a Barbie doll.
41
Forbes (Palmeri 1997) reported that a major retailer determined that customers who buy Barbie dolls
have a 60% likelihood of buying one of three types of candy bars. The confidence of the rule Barbie 
candy is 60%. The retailer was unsure what to do with this nugget. The online newsletter Knowledge
Discovery Nuggets invited suggestions (Piatesky-Shapiro 1998).
Data Capacity
D
A
A
A
A
B C
B B
A
D
A
42
In data mining, the data is not generated to meet the objectives of the analysis. It must be determined
whether the data, as it exists, has the capacity to meet the objectives. For example, quantifying affinities
among related items would be pointless if very few transactions involved multiple items. Therefore, it is
important to do some initial examination of the data before attempting to do association analysis.
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Association Tool Demonstration
Analysis goal:
Explore associations between retail banking
services used by customers.
Analysis plan:





Create an association data source.
Run an association analysis.
Interpret the association rules.
Run a sequence analysis.
Interpret the sequence rules.
43
A bank’s Marketing Department is interested in examining associations between various retail banking
services used by customers. Marketing would like to determine both typical and atypical service
combinations as well as the order in which the services were first used.
These requirements suggest both a market basket analysis and a sequence analysis.
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Market Basket Analysis
The BANK data set contains service information for nearly 8,000 customers. There are three variables in
the data set, as shown in the table below.
Name
Model Role
Measurement Level
Description
ACCOUNT
ID
Nominal
Account Number
SERVICE
Target
Nominal
Type of Service
VISIT
Sequence
Ordinal
Order of Product Purchase
The BANK data set has over 32,000 rows. Each row of the data set represents a customer-service
combination. Therefore, a single customer can have multiple rows in the data set, and each row represents
one of the products he or she owns. The median number of products per customer is three.
The 13 products are represented in the data set using the following abbreviations:
ATM
automated teller machine debit card
AUTO
automobile installment loan
CCRD
credit card
CD
certificate of deposit
CKCRD
check/debit card
CKING
checking account
HMEQLC
home equity line of credit
IRA
individual retirement account
MMDA
money market deposit account
MTG
mortgage
PLOAN
personal/consumer installment loan
SVG
saving account
TRUST
personal trust account
Your first task is to create a new analysis diagram and data source for the BANK data set.
1. Create a new diagram named Associations Analysis to contain this analysis.
2. Select Create Data Source from the Data Sources project property.
3. Proceed to Step 2 of the Data Source Wizard.
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4. Select the BANK table in the AAEM61 library.
5. Proceed to Step 5 of the Data Source Wizard.
6. In Step 5, assign metadata to the table variables as shown below.
An association analysis requires exactly one target variable and at least one ID variable. Both should
have a nominal measurement level. A sequence analysis also requires a sequence variable. It usually
has an ordinal measurement scale.
8.3 Market Basket Analysis (Self-Study)
7. Proceed to Step 7 of the Data Source Wizard.
For an association analysis, the data source should have a role of Transaction.
8. Select Role  Transaction.
9. Select Finish to close the Data Source Wizard.
10. Drag a BANK data source into the diagram workspace.
11. Select the Explore tab and drag an Association tool into the diagram workspace.
12. Connect the BANK node to the Association node.
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13. Select the Association node and examine its Properties panel.
14. The Export Rule by ID property determines whether the Rule-by-ID data is exported from the
node and if the Rule Description table will be available for display in the Results window.
Set the value for Export Rule by ID to Yes.
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Other options in the Properties panel include the following:
 The Minimum Confidence Level specifies the minimum confidence level to generate a rule. The
default level is 10%.
 The Support Type specifies whether the analysis should use the support count or support
percentage property. The default setting is Percent.
 The Support Count specifies a minimum level of support to claim that items are associated (that
is, they occur together in the database). The default count is 2.
 The Support Percentage specifies a minimum level of support to claim that items are associated
(that is, they occur together in the database). The default frequency is 5%. The support percentage
figure that you specify refers to the proportion of the largest single item frequency, and not the end
support.
 The Maximum Items determine the maximum size of the item set to be considered. For example,
the default of four items indicates that a maximum of four items will be included in a single
association rule.

If you are interested in associations that involve fairly rare products, you should consider
reducing the support count or percentage when you run the Association node. If you obtain
too many rules to be practically useful, you should consider raising the minimum support
count or percentage as one possible solution.
Because you first want to perform a market basket analysis, you do not need the sequence
variable.
15. Open the Variables dialog box for the Association node.
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16. Select Use  No for the Visit variable.
17. Select OK to close the Variables dialog box.
18. Run the diagram from the Association node and view the results.
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The Results - Node: Association Diagram window opens with the Statistics Plot, Statistics Line Plot,
Rule Matrix, and Output windows visible.
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19. Maximize the Statistics Line Plot window.
The statistics line plot graphs the lift, expected confidence, confidence, and support for each of the
rules by rule index number.
Consider the rule A  B. Recall the following:
 Support of A  B is the probability that a customer has both A and B.
 Confidence of A  B is the probability that a customer has B given that the customer has A.
 Expected Confidence of A  B is the probability that a customer has B.
 Lift of A  B is a measure of the strength of the association. If Lift=2 for the rule A=>B, then a
customer having A is twice as likely to have B than a customer chosen at random. Lift is the
confidence divided by the expected confidence.
Notice that the rules are ordered in descending order of lift.
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20. To view the descriptions of the rules, select View  Rules  Rule description.
The highest lift rule is checking, and credit card implies check card. This is not surprising given that
many check cards include credit card logos. Notice the symmetry in rules 1 and 2. This is not
accidental because, as noted earlier, lift is symmetric.
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21. Examine the rule matrix.
The rule matrix plots the rules based on the items on the left side of the rule and the items on the right
side of the rule. The points are colored, based on the confidence of the rules. For example, the rules
with the highest confidence are in the column in the picture above. Using the interactive feature of the
graph, you discover that these rules all have checking on the right side of the rule.
Another way to explore the rules found in the analysis is by plotting the Rules table.
8.3 Market Basket Analysis (Self-Study)
22. Select View  Rules  Rules Table. The Rules Table window opens.
23. Select the Plot Wizard icon,
.
24. Choose a three-dimensional scatter plot for the type of chart, and select Next >.
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25. Select the roles X, Y, and Z for the variables SUPPORT, LIFT, and CONF, respectively.
26. Select Finish to generate the plot.
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27. Rearrange the windows to view the data and the plot simultaneously.
Expanding the Rule column in the data table and selecting points in the three-dimensional plot enable
you to quickly uncover high lift rules from the market basket analysis while judging their confidence
and support. You can use WHERE clauses in the Data Options dialog box to subset cases in which
you are interested.
28. Close the Results window.
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Sequence Analysis
In addition to the products owned by its customers, the bank is interested in examining the order in which
the products are purchased. The sequence variable in the data set enables you to conduct a sequence
analysis.
1. Add an Association node to the diagram workspace and connect it to the BANK node.
2. Rename the new node Sequence Analysis.
3. Set Export Rule by ID to Yes.
4. Examine the Sequence panel in the Properties panel.
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The options in the Sequence panel enable you to specify the following properties:
 Chain Count is the maximum number of items that can be included in a sequence. The default value
is 3 and the maximum value is 10.
 Consolidate Time enables you to specify whether consecutive visits to a location or consecutive
purchases over a given interval can be consolidated into a single visit for analysis purposes. For
example, two products purchased less than a day apart might be considered to be a single transaction.
 Maximum Transaction Duration enables you to specify the maximum length of time for a series of
transactions to be considered a sequence. For example, you might want to specify that the purchase
of two products more than three months apart does not constitute a sequence.
 Support Type specifies whether the sequence analysis should use the Support Count or Support
Percentage property. The default setting is Percent.
 Support Count specifies the minimum frequency required to include a sequence in the sequence
analysis when the Sequence Support Type is set to Count. If a sequence has a count less than the
specified value, that sequence is excluded from the output. The default setting is 2.
 Support Percentage specifies the minimum level of support to include the sequence in the analysis
when the Support Type is set to Percent. If a sequence has a frequency that is less than the
specified percentage of the total number of transactions, then that sequence is excluded from the
output. The default percentage is 2%. Permissible values are real numbers between 0 and 100.
5. Run the diagram from the Sequence Analysis node and view the results.
6. Maximize the Statistics Line Plot window.
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The statistics line plot graphs the confidence and support for each of the rules by rule index number.
The percent support is the transaction count divided by the total number of customers, which would
be the maximum transaction count. The percent confidence is the transaction count divided
by the transaction count for the left side of the sequence.
7. Select View  Rules  Rule description to view the descriptions of the rules.
The confidence for many of the rules changes after the order of service acquisition is considered.
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Exercises
2. Conducting an Association Analysis
A store is interested in determining the associations between items purchased from the Health and
Beauty Aids department and the Stationery Department. The store chose to conduct a market basket
analysis of specific items purchased from these two departments. The ASSOCIATIONS data set
contains information about over 400,000 transactions made over the past three months. The following
products are represented in the data set:
1. bar soap
2. bows
3. candy bars
4. deodorant
5. greeting cards
6. magazines
7. markers
8. pain relievers
9. pencils
10. pens
11. perfume
12. photo processing
13. prescription medications
14. shampoo
15. toothbrushes
16. toothpaste
17. wrapping paper
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There are four variables in the data set:
Name
Model
Role
Measurement
Level
Description
STORE
Rejected
Nominal
Identification number of the store
TRANSACTION
ID
Nominal
Transaction identification number
PRODUCT
Target
Nominal
Product purchased
QUANTITY
Rejected
Interval
Quantity of this product purchased
a. Create a new diagram. Name the diagram Transactions.
b. Create a new data source using the data set AAEM61.TRANSACTIONS.
c. Assign the variables STORE and QUANTITY the model role Rejected. These variables will not be
used in this analysis. Assign the ID model role to the variable TRANSACTION and the Target
model role to the variable PRODUCT.
d. Add the node for the TRANSACTIONS data set and an Association node to the diagram.
e. Change the setting for Export Rule by ID to Yes.
f. Leave the remaining default settings for the Association node and run the analysis.
g. Examine the results of the association analysis.
What is the highest lift value for the resulting rules?
Which rule has this value?
8.4 Chapter Summary
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8.4 Chapter Summary
Pattern discovery seems to embody the promise of data mining, but there are many ways for an analysis
to fail. SAS Enterprise Miner provides tools to help with data reduction, novelty detection, profiling,
market basket analysis, and sequence analysis.
Cluster and segmentation analyses are similar in intent but differ in execution. In cluster analysis, the goal
is to identify distinct groupings of cases across a set of inputs. In segmentation analysis, the goal is to
partition cases from a single cluster into contiguous groups.
SAS Enterprise Miner offers several tools for exploring the results of a cluster and segmentation analysis.
For low dimension data, you can use capabilities provided by the Graph Wizard and the Explore window.
For higher dimensional data, you can choose the Segment Profile tool to understand the generated
partitions.
Market basket and sequence analyses are handled by the Association tool. This tool transforms transaction
data sets into rules. The value of the generated rules is gauged by confidence, support, and lift. The
Association tool features a variety of plots and tables to help you explore the analysis results.
Pattern Discovery Tools: Review
Generate cluster models using automatic
settings and segmentation models with
user-defined settings.
Compare within-segment distributions of
selected inputs to overall distributions. This
helps you understand segment definition.
Conduct market basket and sequence analysis
on transactions data. A data source must have
one target, one ID, and (if desired) one
sequence variable in the data source.
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8.5 Solutions
Solutions to Exercises
1. Conducting Cluster Analysis
a. Create a new diagram in your project.
1) To open a diagram, select File  New  Diagram.
2) Type the name of the new diagram, Jeans, and select OK.
b. Define the data set DUNGAREE as a data source.
1) Select File  New  Data Source….
2) In the Data Source Wizard - Metadata Source window, make sure that SAS Table is selected
as the source and select Next >.
3) To choose the desired data table, select Browse….
4) Double-click on the AAEM61 library to see the data tables in the library.
5) Select the DUNGAREE data set, and then select OK.
6) Select Next >.
7) Select Next >.
8) Select Advanced to use the Advanced Advisor, and then select Next >.
8.5 Solutions
c. Determine whether the model roles and measurement levels assigned to the variables are
appropriate.
Examine the distribution of the variables.
1) Hold down the CTRL key and click to select the variables of interest.
2) Select Explore.
3) Select Next > and then Finish to complete the data source creation.
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There do not appear to be any unusual or missing data values.
8.5 Solutions
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d. The variable STOREID should have the ID model role and the variable SALESTOT should have
the Rejected model role.
The variable SALESTOT should be rejected because it is the sum of the other input
variables in the data set. Therefore, it should not be considered as an independent input
value.
e. To add an Input Data node to the diagram workspace and select the DUNGAREE data table
as the data source, drag the DUNGAREE data source onto the diagram workspace.
f. Add a Cluster node to the diagram workspace. The workspace should appear as shown.
g. Select the Cluster node.
In the property sheet, select Internal Standardization  Standardization.
If you do not standardize, the clustering will occur strictly on the inputs with the largest
range (Original and Leisure).
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h. Run the diagram from the Cluster node and examine the results.
1) Run the Cluster node and view the results.
2) To view the results, right-click the Cluster node and select Results….
The Cluster node’s Automatic number of cluster specification method seems to generate an
excessive number of clusters.
8.5 Solutions
i. Specify a maximum of six clusters.
1) Select Specification Method  User Specify.
2) Select Maximum Number of Clusters  6.
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3) Run the Cluster node and view the results.
Apparently, all but one of the segments is well populated. There are more details about
the segment composition in the next step.
j. Connect a Segment Profile node to the Cluster node.
8.5 Solutions
1) Run the Segment Profile node and view the results.
Segment 1 contains stores selling a higher-than-average number of original jeans.
Segment 2 contains stores selling a higher-than-average number of stretch jeans.
Segment 3 contains stores selling small numbers of all jean styles.
Segment 4 contains stores selling a higher-than-average number of leisure jeans.
Segment 5 contains stores selling a higher-than-average number of fashion jeans.
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Segment 6 contains stores selling a higher-than-average number of original jeans, but lowerthan-average number of stretch and fashion.
2. Conducting an Association Analysis
a. Create the Transactions diagram.
1) To open a new diagram in the project, select File  New  Diagram….
2) Name the new diagram Transactions and select OK.
b. Create a new data source using the data set AAEM61.TRANSACTIONS.
1) Right-click Data Sources in the project tree and select Create Data Source.
2) In the Data Source Wizard - Metadata Source window, make sure that SAS Table is selected
as the source and select Next >.
3) Select Browse… to choose a data set.
4) Double-click on the AAEM61 library and select the TRANSACTIONS data set.
5) Select OK.
6) Select Next >.
7) Examine the data table properties, and then select Next >.
8) Select Advanced to use the Advanced Advisor, and then select Next >.
8.5 Solutions
c. Assign appropriate model roles to the variables.
1) Hold down the CTRL key and select the rows for the variables STORE and QUANTITY.
In the Role column of one of these rows, select Rejected.
2) Select the TRANSACTION row and select ID as the role.
3) Select the PRODUCT row and select Target as the role.
4) Select Next >.
5) To skip decision processing, select Next >.
6) Change the role to Transaction.
7) Select Finish.
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d. Add the node for the TRANSACTIONS data set and an Association node to the diagram.
The workspace should appear as shown.
e. Change the setting for Export Rule by ID to Yes.
8.5 Solutions
f. Run the Association node and view the results.
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g. Examine the results of the association analysis.
Examine the Statistics Line plot.
Rule 1 has the highest lift value, 3.60.
Looking at the output reveals that Rule 1 is the rule Toothbrush  Perfume.